IG Established

The evolution of the evaluation of research from being mostly bibliographic index-based is now recognized to be indispensable to enable Open Science. In the same way that Open Science promotes the open sharing of FAIR data and other research outputs, evaluators must also value and consider these outputs as part of the wider research evaluation framework. The RDA is an appropriate forum for addressing the evolving requirements of research evaluation as they relate to Open Science practices.

Lack of interoperability between tools/e-infrastructures presents a significant barrier to streamlining processes throughout the research lifecycle. These gaps prevent the comprehensive collection and incorporation of research data and metadata into the research record captured during the active research phase. Furthermore, it limits the scope for passing this data and metadata on to data repositories, thus undermining FAIR data principles and reproducibility.

The FAIR for Machine Learning (FAIR4ML) Interest Group will enable community members to discuss the various aspects of FAIR as applied to Machine Learning, looking both at domain specific and domain-agnostic use cases, and creating task forces and working groups as needed for specific guidance documents, recommendations, definitions and technical specification to that effect. The overall aim is to foster collaborations among researchers and developers who are interested in making machine learning (data, models, workflows, etc.) FAIR, along with those who contribute to the infrastructure and policies that support this.

Aligning with RDA’s mission, the purpose of the Psychology RDA Interest Group (IG) is to raise awareness of the importance of data sharing, and to provide support with respect to the technical skills needed for FAIR data sharing within the Psychology community. This IG brings together Psychology researchers with an interest in open, transparent, and reproducible research methods and practices.